Playing offense with Data Catalog to score on self-service analytics goal

25 Oct, 2021 •

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Business Teams have access to Data?, self-service analytics platform and tools?, and have hired data analysts and scientists? But decision-makers are drowning in data and starving for insights. The biggest hurdle is the speed of Data Discovery by Analysts and Scientists.  Data Catalogs can help address this challenge by adding definition, context, profile, quality of data. It helps make data easy to find, understand, and trust. In turn, allow Analysts to build and ship analytics faster and deliver timely insights to the business.  

Data is the new currency and it is being generated with increasing speed. Executives in every company and every function know that data is important and it needs to be harnessed to fuel business initiatives.  Without it, there can be no digital transformation to propel the organization past competitors. Companies have invested considerable resources to make businesses capable to generate insights through self-service analytics. Business teams have been given access to data, stood up analytics platforms and tools, and have hired and trained data analysts and scientists. 

Despite all this, decision-makers are drowning in data but starving for insights

In a recent poll by DvSum, more than 60% of analytics experts surveyed identified the time taken for data discovery to be the biggest pain point that business leaders are facing in delivering actionable insights. 

biggest challenge analytics leaders are facing
LinkedIn Poll as of Oct 25, 2021

So, why is Data Discovery slow? 

Because Analysts and Scientists spend 40 – 70% of their time collecting and preparing data to build their BI report or AI models. And why is that?  Because raw data without its definition, profile, quality, and context is difficult to find, understand, and trust. 

Let’s look at an example. If an Analyst is trying to build an Order Shipment Analytics model and he is presented with just a list of datasets. How does he choose the right one? 

blog1 dvsum data without metadata is difficult to find understand trust11
blog1 dvsum data without metadata is difficult to find understand trust21

Below is a typical approach Analysts go through from Data to Analysis. No wonder, Data Analysts spend a lot of time with trial and error before they are ready to analyze or build a model. Moreover, even after the time spent in discovery, the knowledge about data remains tribal. It is not institutionalized and is not re-usable. 

data analysis without data catalog

How can a Data Catalog help? 

Let’s first start by a quick definition of a Data Catalog. It:

  • centralizes the catalog of your data sources and BI Reports – System Catalog
  • it can store additional information about the data – its profile, characteristics, and lineage
  • it allows for enrichment and curation of data – by classifying, linking and tagging data
  • it allows for a common platform for analytics, business SMEs, Data Engineering, IT to collaborate on data 

Data Catalog can help surface the meaning and context of data. 

The same data when available through a catalog is easier to find, understand, and trust

blog1 dvsum data catalogs store metadata about data
blog1 dvsum data catalog fields containing profile quality characteristics of data
Source: DvSum Data Catalog 

With a Data Catalog, the process to build analytics is more like this: 

data analysis with data catalog

In Summary 

A Data Catalog can help analytics teams achieve their self-service analytics goals. It can : 

  •  makes it easy to find, understand and trust data 
  •  reduce data preparation by up to 40% 
  •  scale BI and AI
  •  increase analyst productivity 

In turn, delivering timely insights to the business. 

data catalog benefits

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